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1z0-1127-24 Test Quiz | Reliable 1z0-1127-24 Exam Sample

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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic
Details

Topic 1

Fundamentals of Large Language Models (LLMs): For AI developers and Cloud Architects, this topic discusses LLM architectures and LLM fine-tuning. Additionally, it focuses on prompts for LLMs and fundamentals of code models.

Topic 2

Building an LLM Application with OCI Generative AI Service: For AI Engineers, this section covers Retrieval Augmented Generation (RAG) concepts, vector database concepts, and semantic search concepts. It also focuses on deploying an LLM, tracing and evaluating an LLM, and building an LLM application with RAG and LangChain.

Topic 3

Using OCI Generative AI Service: For AI Specialists, this section covers dedicated AI clusters for fine-tuning and inference. The topic also focuses on the fundamentals of OCI Generative AI service, foundational models for Generation, Summarization, and Embedding.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q29-Q34):

NEW QUESTION # 29
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

A. Assigns a penalty to tokens that have already appeared in the preceding text
B. Determines the maximum number of tokens the model can generate per response
C. Specifies a string that tells the model to stop generating more content
D. Controls the randomness of the model's output, affecting its creativity

Answer: D

Explanation:
The "temperature" parameter in generative AI models controls the randomness of the model's output. It affects the creativity and diversity of the generated text:
Low temperature: Leads to more deterministic and focused outputs, where the model tends to choose the most probable tokens, resulting in less randomness and creativity.
High temperature: Increases randomness by making the probability distribution over the next tokens flatter. This allows for more diverse and creative outputs, as the model is more likely to choose less probable tokens.
Adjusting the temperature parameter enables fine-tuning the balance between creativity and coherence in the model's responses.
Reference
Research articles on the role of temperature in generative models
Technical guides for tuning generative AI models in OCI

NEW QUESTION # 30
What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?

A. Underfitting
B. Model Drift
C. Overfilling
D. Data Leakage

Answer: C

NEW QUESTION # 31
What does a dedicated RDMA cluster network do during model fine-tuning and inference?

A. It limits the number of fine-tuned model deployable on the same GPU cluster.
B. It increases G PU memory requirements for model deployment.
C. It leads to higher latency in model inference.
D. It enables the deployment of multiple fine-tuned models.

Answer: D

NEW QUESTION # 32
What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?

A. Model Drift
B. Overfilling
C. Underfitting
D. Data Leakage

Answer: C

Explanation:
Using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service might result in underfitting. Underfitting occurs when a model is too simplistic to capture the underlying patterns in the data, leading to poor performance on both training and validation data. This is particularly problematic with small data sets because there may not be enough information for the model to learn the necessary patterns and relationships.
Reference
Articles on machine learning challenges with small data sets
Technical documentation on fine-tuning models in OCI

NEW QUESTION # 33
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours arc required for fine-tuning if the cluster is active for 10 hours?

A. 10 unit hours
B. 15 unit hours
C. 40 unit hours
D. 30 unit hours

Answer: A

NEW QUESTION # 34
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